Does logistic regression have a unique decision boundary?

The decision boundary is not unique. Here we show how maximum likelihood estimation for logistic regression can break down when training on linearly separable data.

What decision boundary can logistic regression provide?

The fundamental application of logistic regression is to determine a decision boundary for a binary classification problem. Although the baseline is to identify a binary decision boundary, the approach can be very well applied for scenarios with multiple classification classes or multi-class classification.

What is the main purpose of logistic regression Do you know other regression that can provide similar estimates?

Logistic regression is useful for situations where there could be an ability to predict the presence or absence of a characteristic or outcome, based on values of a set of predictor variables. It is similar to a linear regression model but is suited to models where the dependent variable is dichotomous.

What are the assumptions of logistic regression?

Basic assumptions that must be met for logistic regression include independence of errors, linearity in the logit for continuous variables, absence of multicollinearity, and lack of strongly influential outliers.

How does its decision boundary differ from that of Logistic regression?

Decision Boundaries Decision Trees bisect the space into smaller and smaller regions, whereas Logistic Regression fits a single line to divide the space exactly into two.

Does Logistic regression have linear decision boundary?

Logistic Regression has traditionally been used as a linear classifier, i.e. when the classes can be separated in the feature space by linear boundaries. The decision boundary is thus linear .

What is the advantage of logistic regression?

Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space.

What is the purpose of logistic regression?

The purpose of logistic regression is to estimate the probabilities of events, including determining a relationship between features and the probabilities of particular outcomes.

What are the limitations of logistic regression?

The major limitation of Logistic Regression is the assumption of linearity between the dependent variable and the independent variables. It not only provides a measure of how appropriate a predictor(coefficient size)is, but also its direction of association (positive or negative).

Does logistic regression check for the linear relationship between dependent and independent variables True or false?

First, logistic regression does not require a linear relationship between the dependent and independent variables. Second, the error terms (residuals) do not need to be normally distributed. Finally, the dependent variable in logistic regression is not measured on an interval or ratio scale.

Is Logistic regression better than decision trees?

If you’ve studied a bit of statistics or machine learning, there is a good chance you have come across logistic regression (aka binary logit).

What is the advantage of Logistic regression?

What is the equation for logistic regression?

Using the generalized linear model, an estimated logistic regression equation can be formulated as below. The coefficients a and bk (k = 1, 2., p) are determined according to a maximum likelihood approach, and it allows us to estimate the probability of the dependent variable y taking on the value 1 for given values of xk (k = 1, 2., p).

What is the function of logistic regression?

Logistic Regression uses the logistic function to find a model that fits with the data points. The function gives an ‘S’ shaped curve to model the data. The curve is restricted between 0 and 1, so it is easy to apply when y is binary.

Assumptions of Logistic Regression. This means that the independent variables should not be too highly correlated with each other. Fourth, logistic regression assumes linearity of independent variables and log odds. although this analysis does not require the dependent and independent variables to be related linearly,…

What is penalized logistic regression?

Penalized logistic regression imposes a penalty to the logistic model for having too many variables. This results in shrinking the coefficients of the less contributive variables toward zero.

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